A5101435476- Soil Carbon and Nitrogen Dynamics
- Plant Water Relations and Carbon Dynamics
- Climate variability and models
- Statistical Methods and Inference
- Radioactive element chemistry and processing
- Soil Geostatistics and Mapping
- Nonlinear Dynamics and Pattern Formation
- Statistical Methods and Bayesian Inference
- Soil and Water Nutrient Dynamics
- Chaos control and synchronization
- Soil and Unsaturated Flow
- Geochemistry and Elemental Analysis
- Scientific Computing and Data Management
- Carbon Dioxide Capture Technologies
- Random Matrices and Applications
- Aluminum toxicity and tolerance in plants and animals
- Insurance, Mortality, Demography, Risk Management
- Evaluation Methods in Various Fields
- Psychometric Methodologies and Testing
- Plant nutrient uptake and metabolism
- Complex Network Analysis Techniques
- Evolutionary Game Theory and Cooperation
- Advanced Statistical Modeling Techniques
- Gastroesophageal reflux and treatments
- Copper-based nanomaterials and applications
Dalian Ocean University
2025
NSF National Center for Atmospheric Research
2024
Northern Arizona University
2018-2023
Florida Institute of Technology
2021
Tsinghua University
2017-2018
Fuzhou University
2012
Guangxi Open University
2010
Amgen (United States)
2010
Tonghua Normal University
2007
Abstract Nitrogen immobilization usually leads to nitrogen retention in soil and, thus, influences supply for plant growth. Understanding is important predicting cycling under anthropogenic activities and climate changes. However, the global patterns drivers of remain unclear. We synthesized 1350 observations gross rate (NIR) from 97 articles identify NIR. The mean NIR was 8.77 ± 1.01 mg N kg −1 day . It 5.55 0.41 croplands, 15.74 3.02 wetlands, 15.26 2.98 forests. increased with annual...
This article studies a general joint model for longitudinal measurements and competing risks survival data. The consists of linear mixed effects sub-model the outcome, proportional cause-specific hazards frailty data, regression variance–covariance matrix multivariate latent random based on modified Cholesky decomposition. provides useful approach to adjust non-ignorable missing data due dropout enables analysis outcome with informative censoring intermittently measured time-dependent...
Abstract. The concentration–carbon feedback (β), also called the CO2 fertilization effect, is a key unknown in climate–carbon-cycle projections. A better understanding of model mechanisms that govern terrestrial ecosystem responses to elevated urgently needed enable more accurate prediction future carbon sink. We conducted C-only, carbon–nitrogen (C–N) and carbon–nitrogen–phosphorus (C–N–P) simulations Community Atmosphere Biosphere Land Exchange (CABLE) from 1901 2100 with fixed climate...
Abstract. Soil organic carbon (SOC) has a significant effect on emissions and climate change. However, the current SOC prediction accuracy of most models is very low. Most evaluation studies indicate that error mainly comes from parameter uncertainties, which can be improved by calibration. Data assimilation techniques have been successfully employed for calibration models. data algorithms, such as sampling-based Bayesian Markov chain Monte Carlo (MCMC), generally high computation costs are...
This article proposes a joint model for longitudinal measurements and competing risks survival data. The consists of linear mixed effects sub-model with t-distributed measurement errors the outcome, proportional cause-specific hazards frailty regression variance-covariance matrix multivariate latent random based on modified Cholesky decomposition. A Bayesian MCMC procedure is developed parameter estimation inference. Our method insensitive to outlying in presence non-ignorable missing data...
Abstract. Models are an important tool to predict Earth system dynamics. An accurate prediction of future states ecosystems depends on not only model structures but also parameterizations. Model parameters can be constrained by data assimilation. However, applications assimilation ecology restricted highly technical requirements such as model-dependent coding. To alleviate this burden, we developed a model-independent (MIDA) module. MIDA works in three steps including preparation, execution...
With the development of education and open software, item response model is more important in testing actual testees’ abilities. In model, abilities parameters are interrelated. Bayesian method estimation but calculation difficulty. This paper use Scilab Mysql database construct parameter framework; provideBayesian joint likelihood with Langevin MCMC method. Compare three ways to calculate abilities, including random walk sampling, simple sampling target distribution methods.Through...
The mean hitting time of a Markov chain on graph from an arbitrary node to target randomly chosen according its stationary distribution is called Kemeny's constant, which important metric for network analysis and has wide range applications. It is, however, still computationally expensive evaluate the especially when it comes large graph, since requires computation spectrum corresponding transition matrix or normalized Laplacian matrix. In this paper, we propose simple yet efficient Monte...
Abstract:
We investigate the emergence of target waves in a cyclic predator-prey model incorporating periodic current three competing species small area situated at center square lattice. The acts as pacemaker, trying to impose its rhythm on overall spatiotemporal evolution species. show that pacemaker is able nucleate eventually spread across whole population, whereby routes leading this phenomenon can be distinguished depending mobility and oscillation period localized current. First, emerge due...
Abstract. Soil organic carbon (SOC) has a significant effect on the emission and climate change. However, current SOC prediction accuracy of most models is very low. Most evaluation studies indicate that error mainly comes from parameter uncertainties, which can be obviously improved by calibration. Data assimilation technique been successfully employed for calibration models. data algorithms such as Bayesian Markov Chain Monte Carlo (MCMC) generally require large amount computation cost are...